There is a great deal of literature regarding use of non-spatial weight matrices or combinations of spatial and non-spatial weight structures. We explore alternative approaches for constructing convex combinations of different types of dependence between observations. Pace and LeSage (2002) as well as Hazır, LeSage and Autant-Bernard (2016) use convex combinations of different weight matrices to form a single weight matrix that can be used in conventional spatial regression estimation and inference. We explore issues that arise in producing estimates and inferences from these more general cross-sectional regression relationships in a Bayesian framework. We propose two procedures to estimate such models and assess their finite sample properties through Monte Carlo experiments. Lastly, we apply our methodology to CEOs’ salaries of Texan nursing homes. Literature has shown that CEOs’ salaries were not fixed independently between nursing homes. We consider 2 measures of similarity between them, namely geographic and peer proximity. We find that the peer effect is relatively more relevant (87%) to explain mimicking behavior in the fixing of CEOs’ wage than geographic proximity (13%), the latter remaining nevertheless significant.

Speaker Introduction:

Dr. Debarsy is a CNRS research fellow at Université de Lille Sciences et Technologies in France. He obtained his Ph.D. in Economics from University of Namur in 2011. His research area includes spatial econometrics and regional economics. His research has appeared on journals such as Journal of Econometrics.